Integrated biosensor platform based on graphene transistor arrays for real-time high-accuracy ion sensing
<jats:title>Abstract</jats:title><jats:p>Two-dimensional materials such as graphene have shown great promise as biosensors, but suffer from large device-to-device variation due to non-uniform material synthesis and device fabrication technologies. Here, we develop a robust bioelect...
| Main Authors: | , , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer Science and Business Media LLC
2022
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| Online Access: | https://hdl.handle.net/1721.1/145510 |
| _version_ | 1826208288767737856 |
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| author | Xue, Mantian Mackin, Charles Weng, Wei-Hung Zhu, Jiadi Luo, Yiyue Luo, Shao-Xiong Lennon Lu, Ang-Yu Hempel, Marek McVay, Elaine Kong, Jing Palacios, Tomás |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Xue, Mantian Mackin, Charles Weng, Wei-Hung Zhu, Jiadi Luo, Yiyue Luo, Shao-Xiong Lennon Lu, Ang-Yu Hempel, Marek McVay, Elaine Kong, Jing Palacios, Tomás |
| author_sort | Xue, Mantian |
| collection | MIT |
| description | <jats:title>Abstract</jats:title><jats:p>Two-dimensional materials such as graphene have shown great promise as biosensors, but suffer from large device-to-device variation due to non-uniform material synthesis and device fabrication technologies. Here, we develop a robust bioelectronic sensing platform composed of more than 200 integrated sensing units, custom-built high-speed readout electronics, and machine learning inference that overcomes these challenges to achieve rapid, portable, and reliable measurements. The platform demonstrates reconfigurable multi-ion electrolyte sensing capability and provides highly sensitive, reversible, and real-time response for potassium, sodium, and calcium ions in complex solutions despite variations in device performance. A calibration method leveraging the sensor redundancy and device-to-device variation is also proposed, while a machine learning model trained with multi-dimensional information collected through the multiplexed sensor array is used to enhance the sensing system’s functionality and accuracy in ion classification.</jats:p> |
| first_indexed | 2024-09-23T14:03:29Z |
| format | Article |
| id | mit-1721.1/145510 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T14:03:29Z |
| publishDate | 2022 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1455102022-09-28T18:02:20Z Integrated biosensor platform based on graphene transistor arrays for real-time high-accuracy ion sensing Xue, Mantian Mackin, Charles Weng, Wei-Hung Zhu, Jiadi Luo, Yiyue Luo, Shao-Xiong Lennon Lu, Ang-Yu Hempel, Marek McVay, Elaine Kong, Jing Palacios, Tomás Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Department of Chemistry Massachusetts Institute of Technology. Institute for Soldier Nanotechnologies <jats:title>Abstract</jats:title><jats:p>Two-dimensional materials such as graphene have shown great promise as biosensors, but suffer from large device-to-device variation due to non-uniform material synthesis and device fabrication technologies. Here, we develop a robust bioelectronic sensing platform composed of more than 200 integrated sensing units, custom-built high-speed readout electronics, and machine learning inference that overcomes these challenges to achieve rapid, portable, and reliable measurements. The platform demonstrates reconfigurable multi-ion electrolyte sensing capability and provides highly sensitive, reversible, and real-time response for potassium, sodium, and calcium ions in complex solutions despite variations in device performance. A calibration method leveraging the sensor redundancy and device-to-device variation is also proposed, while a machine learning model trained with multi-dimensional information collected through the multiplexed sensor array is used to enhance the sensing system’s functionality and accuracy in ion classification.</jats:p> 2022-09-19T19:08:05Z 2022-09-19T19:08:05Z 2022-08-27 2022-09-19T18:56:08Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/145510 Xue, Mantian, Mackin, Charles, Weng, Wei-Hung, Zhu, Jiadi, Luo, Yiyue et al. 2022. "Integrated biosensor platform based on graphene transistor arrays for real-time high-accuracy ion sensing." Nature Communications, 13 (1). en 10.1038/s41467-022-32749-4 Nature Communications Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Springer Science and Business Media LLC Nature |
| spellingShingle | Xue, Mantian Mackin, Charles Weng, Wei-Hung Zhu, Jiadi Luo, Yiyue Luo, Shao-Xiong Lennon Lu, Ang-Yu Hempel, Marek McVay, Elaine Kong, Jing Palacios, Tomás Integrated biosensor platform based on graphene transistor arrays for real-time high-accuracy ion sensing |
| title | Integrated biosensor platform based on graphene transistor arrays for real-time high-accuracy ion sensing |
| title_full | Integrated biosensor platform based on graphene transistor arrays for real-time high-accuracy ion sensing |
| title_fullStr | Integrated biosensor platform based on graphene transistor arrays for real-time high-accuracy ion sensing |
| title_full_unstemmed | Integrated biosensor platform based on graphene transistor arrays for real-time high-accuracy ion sensing |
| title_short | Integrated biosensor platform based on graphene transistor arrays for real-time high-accuracy ion sensing |
| title_sort | integrated biosensor platform based on graphene transistor arrays for real time high accuracy ion sensing |
| url | https://hdl.handle.net/1721.1/145510 |
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